Oral squamous cell carcinoma (OSCC) is one of the most common lip and oral cavity cancer types. It requires early detection via various medical technologies to improve the survival rate. While most detection techniques for OSCC require testing in a centralized lab to confirm cancer type, a point of care detection technique is preferred for on-site use and quick result readout. The modular biological sensor utilizing transistor-based technology has been leveraged for testing CIP2A, and optimal transistor gate voltage and load resistance for sensing setup was investigated. Sensitivities of 1 × 10−15 g/ml have been obtained for both detections of pure CIP2A protein and HeLa cell lysate using identical test conditions via serial dilution. The superior time-saving and high accuracy testing provides opportunities for rapid clinical diagnosis in the medical space.

The SARS-CoV-2 pandemic has had a significant impact worldwide. Currently, the most common detection methods for the virus are polymerase chain reaction (PCR) and lateral flow tests. PCR takes more than an hour to obtain the results and lateral flow tests have difficulty with detecting the virus at low concentrations. In this study, 60 clinical human saliva samples, which included 30 positive and 30 negative samples confirmed with RT-PCR, were screened for COVID-19 using disposable glucose biosensor strips and a reusable printed circuit board. The disposable strips were gold plated and functionalized to immobilize antibodies on the gold film. After functionalization, the strips were connected to the gate electrode of a metal-oxide-semiconductor field-effect transistor on the printed circuit board to amplify the test signals. A synchronous double-pulsed bias voltage was applied to the drain of the transistor and strips. The resulting change in drain waveforms was converted to digital readings. The RT-PCR-confirmed saliva samples were tested again using quantitative PCR (RT-qPCR) to determine cycling threshold (Ct) values. Ct values up to 45 refer to the number of amplification cycles needed to detect the presence of the virus. These PCR results were compared with digital readings from the sensor to better evaluate the sensor technology. The results indicate that the samples with a range of Ct values from 17.8 to 35 can be differentiated, which highlights the increased sensitivity of this sensor technology. This research exhibits the potential of this biosensor technology to be further developed into a cost-effective, point-of-care, and portable rapid detection method for SARS-CoV-2.

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